B-SIFT: a highly efficient binary SIFT descriptor for invariant feature correspondence

Authors:
Jing Li;Zhaoyang Lu
Affiliations:
The State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering, Xidian University, Xi'an, China;The State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering, Xidian University, Xi'an, China
Venue:
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2011

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Abstract

This paper presents a novel binary descriptor named as B-SIFT(Binarized Scale Invariant Feature Transform) for efficient invariant feature correspondence.Through analyzing the local distinctive gradient pattern, we convert the standard SIFT descriptor to a binary representation which can be computed extremely fast with bitwise operation. Extensive correspondence trials based on a benchmark Oxford image data set with viewpoint, scale, image blur, JPEG compression and illumination changes demonstrate that in general, the proposed B-SIFT method significantly outperforms the standard SIFT with over 400 times faster in matching time and 32 times less in memory resources, while achieves the same matching score as SIFT.